STA5176: Statistical Modeling - Fall 2022

Author

Dr. Samantha Seals

Published

August 23, 2022

Instructor Information

  • Dr. Samantha Seals
  • Email: sseals@uwf.edu
  • Office: 4/344

Meeting Times and Location

  • MW 4:00-5:15 pm (Central)
  • Zoom: Class Zoom
  • Campus: 4/406

Online Office Hours

Regular weekly online office hours will be held:

Day Time
Monday 12:00-3:00 pm (Central)
Wednesday 12:00-3:00 pm (Central)

Zoom for office hours: Office Zoom. If meeting on Zoom, you must schedule an appointment through Navigate (instructions here). Note that Navigate looks at (1) your class schedule, (2) my Google Calendar to look for times we are both available. Appointments outside of available time slots may be made through email. Any changes to regular office hours will be announced on Canvas.

Class Websites

Discord server: Statistical Modeling - Fall 2022

Website with schedule: https://samanthaseals.github.io/STA5176/

GitHub repository: https://github.com/samanthaseals/STA5176

Course Description

This course will provide further examination of statistics and data analysis beyond an introductory course. Topics covered include data visualization, point and interval estimation, hypothesis testing of means, variances, and proportions, and linear and logistic regressions. Emphasis will be placed on conducting reproducible research.

Topics

  • reproducible research
  • data visualization
  • estimation and inference for means, medians, variances, and proportions
  • linear regression
  • goodness of fit
  • contingency tables
  • chi-squared test for independence
  • Fisher’s exact test
  • logistic regression

Student Learning Outcomes

  • Demonstrate the ability to construct point and interval estimates
  • Demonstrate the ability to perform the appropriate hypothesis test based on the available data and research question
  • Demonstrate the ability to draw statistical conclusions based on hypothesis testing
  • Demonstrate the ability to construct appropriate data visualization
  • Demonstrate the ability to use statistical software for analysis and construct professional reports incorporating principles of reproducible research

Course Materials

Required Textbook:

An Introduction to Statistical Methods and Data Analysis, R.L. Ott and M.L Longnecker, 7th edition

Supplemental Texts:

The following are recommended as reference books for your enrichment both within this course and during your time as a graduate student.

R Cookbook, Paul Teetor

Data Visualization, Kieran Healy

Course Format and Organization

This course will be taught synchronously via Zoom You may join the classroom here: Class Zoom.

Note that all lectures will be recorded; if you choose to have your video on, you may show up on the recording. If you choose to use the microphone to ask questions, your voice will be captured on the recording. If you do not feel comfortable with either of these items, please do not turn on your video and do not use the microphone to ask questions. Lecture notes and videos will be posted to Canvas within two business days. You will be able to watch videos on Canvas, however, videos are not downloadable by students.

Each week in STA5176 will, for the most part, look something like this:

Attendance is essential to student success in this course. Though attendance will not be explicitly reflected in your grade, I expect all students to attend (or watch) every lecture. While Canvas will be updated on a regular basis, it is important to note that it may not be immediately updated.

Questions about homework or class material should be directed to the Discord server. While students are expected to respond and engage with one another, I will monitor responses for accuracy.

Grading and Evaluation

The course grade will be determined as follows:

  1. Daily Activities (40%): Every lecture will be accompanied with a short activity to be completed in RStudio by the student. The resulting .html file will be submitted to the appropriate dropbox on Canvas. To allow for flexibility with life/work scheduling, these activities will be turned in the following day, by 11:59 pm (Central). The activities and corresponding due dates are listed on our class website.
  2. Projects (40%): All projects will be completed using R and a report will be constructed using Quarto. Projects will be submitted as .html files to the appropriate dropbox on Canvas. The projects and corresponding due dates are listed on our class website. Loosely, the projects will cover Chapters 5, 6, 7 (P1 - one and two sample tests); Chapters 8, 9, 14 (P2 - one-way and two-way ANOVA); Chapters 11, 12 (P3 - linear regression); and Chapter 10 (P4 - categorical analysis).
  3. Final Exam (20%): The final exam will be a timed concepts-based exam (20%). While there may be some calculations needed, you will not be processing raw data on the in-class portion of the final exam. The final exam will be open only on Wednesday, December 7, 2022. The date of this exam will not change, so please make arrangements to be free for a 3 hour block on the specified date.

It is expected that all work submitted is the student’s own work. Evidence of academic dishonesty, including using “homework help” websites such as Chegg or CourseHero, or collaboration with other students, will be submitted to the Dean of Students. A grade of 0 will automatically be assigned for the assignment and there will be no opportunities to change that grade.

Late Policy

It is always my goal to provide you with timely feedback, hence, the necessity of deadlines for the projects. However, I recognize that life happens and allow for an extension. Please send me an email prior to the deadline to let me know that you need an extension. Deadlines will not be pushed more than one week unless necessary. You do not have to justify your need for an extension, but you are welcome to do so.

Course Grade

Final course grades will be determined according to the following scale. Conventional rounding rules will be applied.

Letter Grade Weighted Score
A 93%–100%
A- 90%–92%
B+ 87%–89%
B 83%–86%
B- 80%–82%
C+ 77%–79%
C 73%–76%
C- 70%–72%
D+ 67%–69%
D 60%–66%
F < 60%

Important University Dates

Date Event
August 22 (Mon) Fall semester begins.
August 28 (Sun) Drop/Add period ends.
September 5 (Mon) Labor Day Holiday; campus closed.
November 11 (Fri) Veteran’s Day Holiday; campus closed.
November 14 (Mon) Withdrawal deadline (automatic grade of “W”).
November 24-25 (Thur-Fri) Thanksgiving Holiday; campus closed.
December 5-9 (Mon-Thur) Finals week.
December 9 (Fri) Late withdrawal deadline (“W” or “WF”, see requirements below).

Students who are requesting a late withdrawal from class must have the approval of the advisor, instructor, and department chairperson (in that order) and finally, by the Academic Appeals committee. Requests for late withdrawals may be approved only for the following reasons (which must be documented):

  • A death in the immediate family.
  • Serious illness of the student or an immediate family member.
  • A situation deemed similar to categories 1 and 2 by all in the approval process.
  • Withdrawal due to Military Service (Florida Statute 1004.07)
  • National Guard Troops Ordered into Active Service (Florida Statute 250.482)

Requests without documentation should not be accepted. A request for a late withdrawal simply for not succeeding in a course does not meet the criteria for approval and should not be approved.

Additional Information for Students

Please see the University’s Confluence page for additional syllabus statements: https://confluence.uwf.edu/display/public/Additional+Syllabus+Statements